Image blur is a significant factor in assessing the perceptual quality of digital images. For example, when using a digital camera to capture digital still images, or a digital camera or digital recording device to capture digital image sequences, image blur can result from the camera or recording device lenses not being properly focused on the subject of the image or image sequence, aberrations in the camera or recording device lenses, aperture blur in the foreground and/or the background of the image or image sequence, the relative motion between the camera or recording device and the subject of the image or image sequence, atmospheric haze, etc. Image blur in digital images can also result from the attenuation of high spatial frequencies caused by digital filtering or block discrete cosine transform (BDCT) based image compression processes. For example, image blur in digital still images compressed according to the Joint Photographic Experts Group (JPEG) standards, and in digital image sequences compressed according to the Moving Picture Experts Group (MPEG) or Telecommunication Standardization Sector of the International Telecommunication Union (ITU-T) H.26n standards, can be caused by quantization which typically follows the BDCT and truncates the high frequency discrete cosine transform (DCT) coefficients. Such loss of high frequency DCT coefficients in the image compression process can appear as image blur in the JPEG images and MPEG or ITU-T H.26n image sequences.
One known technique for measuring image blur in digital images is performed in the spatial domain. This technique typically detects one or more sharp edges in a digital image, and employs pixels in the detected sharp edges to measure the spread of each edge pixel. These measurements are subsequently used to estimate the amount of image blur in the digital image. This technique has drawbacks, however, in that the digital image may not contain any edges sharp enough to estimate image blur with a high level of accuracy.
Another known technique makes some assumptions about the edges in the image when estimating the amount of image blur. However, this technique has also drawbacks, notably, that relying on assumptions about the edges in a digital image when estimating the amount of image blur can further reduce the accuracy of the blur estimate.
Still another known technique employs the local power spectrum of a digital image in the frequency domain to provide a measurement of image blur. In accordance with this technique, an assumption is made that, compared to the local power spectrum of a non-blurred digital image, the local power spectrum of a blurred digital image typically falls more rapidly as the frequency increases. Such techniques for estimating image blur in digital images from the power spectrum of the image also have drawbacks, however, due to their high computational complexity.
Yet another known technique for measuring image blur in digital images includes processing the image data using a DCT to produce a plurality of DCT coefficients—including a number of non-zero DCT coefficients—and generating a weighted occurrence histogram of all of the non-zero DCT coefficients. The amount of image blur in the digital image is then estimated from the weighted occurrence histogram. However, like the technique described above that estimates image blur in a digital image from the power spectrum of the image, this technique for estimating image blur from the weighted occurrence histogram also suffers from a high level of computational complexity.
It would therefore be desirable to have systems and methods of measuring image blur in digital images and digital image sequences that avoid one or more of the drawbacks of known techniques for measuring image blur in digital images and digital image sequences.